A Novel Multivariate Bi-LSTM model for Short-Term Equity Price Forecasting
| Autor: | Oak, Omkar, Nazre, Rukmini, Budke, Rujuta, Mahatekar, Yogita |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Prediction models are crucial in the stock market as they aid in forecasting future prices and trends, enabling investors to make informed decisions and manage risks more effectively. In the Indian stock market, where volatility is often high, accurate predictions can provide a significant edge in capitalizing on market movements. While various models like regression and Artificial Neural Networks (ANNs) have been explored for this purpose, studies have shown that Long Short-Term Memory networks (LSTMs) are the most effective. This is because they can capture complex temporal dependencies present in financial data. This paper presents a Bidirectional Multivariate LSTM model designed to predict short-term stock prices of Indian companies in the NIFTY 100 across four major sectors. Both Univariate LSTM and Univariate Bidirectional LSTM models were evaluated based on R2 score, RMSE, MSE, MAE, and MAPE. To improve predictive accuracy, the analysis was extended to multivariate data. Additionally, 12 technical indicators, having high correlation values with the close price(greater than 0.99) including EMA5, SMA5, TRIMA5, KAMA10 and the Bollinger Bands were selected as variables to further optimize the prediction models. The proposed Bidirectional Multivariate LSTM model, when applied to a dataset containing these indicators, achieved an exceptionally high average R2 score of 99.4779% across the four stocks, which is 3.9833% higher than that of the Unidirectional Multivariate LSTM without technical indicators. The proposed model has an average RMSE of 0.0103955, an average MAE of 0.007485 and an average MAPE of 1.1635%. This highlights the model's exceptional forecasting accuracy and emphasizes its potential to improve short-term trading strategies. Comment: Paper Accepted for presentation at 5th IEEE Global Conference for Advancement in Technology (GCAT) 2024 |
| Databáze: | arXiv |
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